26 research outputs found
Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks
A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition
of multiple MR pulse sequences, which are required for a reliable diagnosis.
Each sequence can be parameterized through multiple acquisition parameters
affecting MR image contrast, signal-to-noise ratio, resolution, or scan time.
With the rise of generative deep learning models, approaches for the synthesis
of MR images are developed to either synthesize additional MR contrasts,
generate synthetic data, or augment existing data for AI training. However,
current generative approaches for the synthesis of MR images are only trained
on images with a specific set of acquisition parameter values, limiting the
clinical value of these methods as various sets of acquisition parameter
settings are used in clinical practice. Therefore, we trained a generative
adversarial network (GAN) to generate synthetic MR knee images conditioned on
various acquisition parameters (repetition time, echo time, image orientation).
This approach enables us to synthesize MR images with adjustable image
contrast. In a visual Turing test, two experts mislabeled 40.5% of real and
synthetic MR images, demonstrating that the image quality of the generated
synthetic and real MR images is comparable. This work can support radiologists
and technologists during the parameterization of MR sequences by previewing the
yielded MR contrast, can serve as a valuable tool for radiology training, and
can be used for customized data generation to support AI training
XFM-guided delivery of imaging-visible human mesenchymal stem cells into the pericardial space in a porcine model
Evaluation of semi quantitative perfusion parameter maps generated based on a fully automated non-rigid motion correction during a first pass myocardial perfusion (FPMP) MRI
A novel, automated method for measuring mitral valve annular velocity from standard cine TrueFISP data - a feasibility study
SNR improvement in GRE-EPI first-pass myocardial perfusion images with non-rigid body registration and KLT filtering
MR-contrast-aware image-to-image translations with generative adversarial networks
Purpose!#!A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required.!##!Methods!#!Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the 'style' for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on.!##!Results!#!This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly.!##!Conclusion!#!Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images